Evolang XV - Exploring the sound structure of novel vocalizations

1 Introduction

This is an RMarkdown accompanying paper ‘Exploring the sound structure of novel vocalizations’ submitted for Evolang 2024. Here we provide the script for similarity analysis of sound signal.

Cite: Fuchs, S., Kadavá, Š., Pouw, W., Walker, B., Fay, N., Winter, B. & Cwiek, A.(2024) Exploring the sound structure of novel vocalizations. Proceedings of the 15th International Conference on the Evolution of Language (EvoLang XV)

First we set up our folders

# get current working directory

curfolder   <- dirname(getwd())
project_folder <- paste0(curfolder, "/sound_similarity/")
dataset_folder <- paste0(project_folder, "/datasets/")
plots_folder <- paste0(project_folder, "/plots/")
audio_orig <- paste0(project_folder, "data/")

# sanity check
#print(curfolder)


# packages

library(soundgen) # sound analysis
library(readr)    # data wrangling
library(tidyr)
library(dplyr)
library(umap) # umap 
library(ggplot2) # plotting
library(viridis) # plotting
library(ggforce) # 
library(plotly) # interactive plots

Then we gather all data that we will later use for analysis, including the wav files.

# these are the original wav files shall we need them
audio_detailed <- list.files(audio_orig,
                         pattern = "*.wav",
                         recursive = TRUE,
                         all.files = FALSE,
                         full.names = TRUE)

# these are the cutted segments we use for the analysis
audio_path  <- paste0(project_folder, "data/segments/")
audio_files <- list.files(audio_path,
                         pattern = "*.wav",
                         recursive = TRUE,
                         all.files = FALSE,
                         full.names = TRUE)

# in this file we store the information about duration of each segment
txt_path <- paste0(project_folder, "data/duration_log_final.txt")

# read it in as a df
data_df <- read.table(txt_path, header = FALSE, sep = '\t')

# inspect the df
head(data_df)

# what is the minimum duration
min(data_df$V5) #18.323 ms

## [1] 18.323

mean(data_df$V5) # 339.914 ms

## [1] 339.9139

2 Extracting features from wav files

We use package ‘soundgen’ to extract a package of features for sound signal More info here: https://www.rdocumentation.org/packages/soundgen/versions/2.6.0 or here: https://cogsci.se/soundgen/acoustic_analysis.html

Now we extract summary of features in segments

# this is an empty data frame where we store the features
feature_df <- data.frame()

# this function extracts summary features from the analyze output
extract_summary_features <- function(file, analyze_result) {
  summary_features <- analyze_result$summary
  # add the filename as the first column
  summary_features <- cbind(File = file, summary_features)
  return(summary_features)
}

# loop over the .wav files in the directory and extract summary features
for (file in audio_files) {
  # skip pauses in segments
  if (endsWith(file, "_p .wav")) {
    print(paste("Skipping file:", file))
    next  
  }
  print(paste("Analyzing file:", file))
  #audio <- readWave(file)
  roughSet <- list(windowLength=10, step = 3, amRes = 10)
  features <- analyze(file, windowLength = 10, step = 5, roughness = roughSet, pitchCeiling=1000, cutFreq=500)
  # sampling rate needed only if x is numeric vector
  # windowLength 10 ms, the minimum duration of a segment is 18 ms
  
  # extract features
  summary_features_df <- extract_summary_features(file, features)
  
  # append the summary features for this file to the main data frame
  feature_df <- rbind(feature_df, summary_features_df)
}

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## [1] "Analyzing file: C:/Users/Sarka Kadava/OneDrive - Leibniz-ZAS/Dokumente/GitHub/sound_similarity/data/segments/p42_t5_v_w123_disgusted_s1 .wav"
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## [1] "Analyzing file: C:/Users/Sarka Kadava/OneDrive - Leibniz-ZAS/Dokumente/GitHub/sound_similarity/data/segments/p43_t40_v_w121_angry_s1 .wav"
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## [1] "Analyzing file: C:/Users/Sarka Kadava/OneDrive - Leibniz-ZAS/Dokumente/GitHub/sound_similarity/data/segments/p45_t25_v_w31_good_s1 .wav"
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# reset row names and remove them
row.names(feature_df) <- NULL

# let's inspect the data frame
#print(feature_df)

# and save it
output_file <- paste0(dataset_folder, "/segments_soundgen_summary.csv")
write.csv(feature_df, file = output_file, row.names = FALSE)

Load in the dataset if you do not have it in the environment. Skip this step if you have it in your enviroment

dataset_path <- file.path(dataset_folder, "/segments_soundgen_summary.csv")
data <- read_csv(dataset_path)

## Rows: 738 Columns: 165
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (2): File, file
## dbl (160): duration, duration_noSilence, voiced, voiced_noSilence, amEnvDep_...
## lgl   (3): fmPurity_mean, fmPurity_median, fmPurity_sd
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

3 Data wrangling

Before we start to compute the distances, we need to prepare the dataframe to more manageable form.

# get the info about each trial
data <- data %>%
  separate(file, into = c("participant", "trial", "condition", "wordCount", "word", "segmentCount"), sep = "_")

# remove '.wav' from the 'segmentnumber' column
data$segmentCount <- gsub("\\ .wav", "", data$segmentCount)

# some of the output of the soundgen function results in NAs, we can get rid of them 
na_only_unique_cols <- sapply(data, function(x) length(unique(x, na.rm = TRUE)) == 1 && all(is.na(unique(x, na.rm = TRUE))))

# delete those columns
cols_to_delete <- names(na_only_unique_cols[na_only_unique_cols])
data <- data %>%
  select(-all_of(cols_to_delete))

# we also remove some other features because they are irrelevant for our case
additional_cols_to_remove <- c('flux_mean', 'flux_median', 'flux_sd', 'fmDep_mean', 'fmDep_median', 'fmDep_sd', 'fmFreq_mean', 'fmFreq_median', 'fmFreq_sd', 'harmEnergy_mean', 'harmEnergy_median', 'harmEnergy_sd', 'harmHeight_mean', 'harmHeight_median', 'harmHeight_sd')

data <- data %>%
  select(-all_of(additional_cols_to_remove))

Now we also need to get some information about the concepts that the segments belong to

# mapping between concepts and categories
concept_category_mapping <- data.frame(
  concept = c("no", "happy", "sad", "bad", "good", "angry", "disgusted", "dog", "cat", "bird", "fish", "fly", "old", "spoon", "egg", "ash", "stone-rock", "smoke", "maybe", "not", "scared"),
  category = c("other", "emotion/valence", "emotion/valence", "emotion/valence", "emotion/valence", "emotion/valence", "emotion/valence", "animal", "animal", "animal", "animal", "animal", "time", "thing/object", "thing/object", "thing/object", "thing/object", "thing/object", "abstract/logical", "abstract/logical", "emotion/valence"),
  stringsAsFactors = FALSE
)

# add this mapping to our df
data <- merge(data, concept_category_mapping, by.x = "word", by.y = "concept", all.x = TRUE)

# now we can remove the file name from df
data <- data %>%
  select(-File)

Additionaly, we decided to take into account only mean and sd, as the mean might be more representative of the variability within the segment then a median (since it’s not always linear)

# get the column names and keep only those that do not contain 'median'
column_names <- names(data)
columns_to_keep <- !grepl("_median", column_names)
data <- data[, columns_to_keep]

Let’s also remove columns where the values are mostly NAs, they are not really informative

# function to remove columns with NA values
remove_columns_with_na <- function(dataset) {
  # column indices with NAs
  na_columns <- apply(is.na(dataset), 2, any)
  
  # remove columns with NAs
  cleaned_dataset <- dataset[, !na_columns]
  
  return(cleaned_dataset)
}

# remove columns with NAs
data <- remove_columns_with_na(data)

# we also got some warning for roughness, so let's just delete them
column_names <- names(data)
columns_to_keep <- !grepl("roughness", column_names)
data <- data[, columns_to_keep]

4 UMAP

Now we can finally run UMAP to reduce the multidimensional space defined by all the features. For more information about UMAP see:

# what are the features
feats <- data[,7:ncol(data)]

# delete the category
feats <- feats %>%
  select(-category)

feats_scaled <- scale(feats) # normalize the features
feats_scaled[is.na(feats_scaled)] <- 0 # if value is NA, replace it by 0

# perform umap
umap_result <- umap(feats_scaled, n_components = 2) 

# add umap coordinates to the feature data
umap_df <- data.frame(umap1 = umap_result$layout[, 1], umap2 = umap_result$layout[, 2], concept = data$word, participant = data$participant, category = data$category, segment = data$segmentCount, trial = data$trial)


# save this df
output_file <- paste0(dataset_folder, "/umap_df.csv")
write.csv(umap_df, file = output_file, row.names = FALSE)


################################

# plot umap, displaying categories
umap1 <- ggplot(umap_df, aes(x = umap1, y = umap2, color = category, text = participant)) +
  geom_point(alpha = 0.7, size = 3) +  
  labs(title = "UMAP Plot of Vocal Segments", x = "UMAP Dimension 1", y = "UMAP Dimension 2") +
  scale_color_viridis(discrete = TRUE) +
  theme_minimal() + 
  theme(plot.title = element_text(hjust = 0.5)) 
ggplotly(umap1)

# save this plot
plot_filename <- file.path(plots_folder, "Umap_cat.png") 
ggsave(plot_filename, umap1, width = 8, height = 6, dpi = 300) 


# plot umap, displaying words
umap2 <- ggplot(umap_df, aes(x = umap1, y = umap2, color = concept, text = participant)) +
  geom_point(alpha = 0.7, size = 3) + 
  labs(title = "UMAP Plot of Vocal Segments", x = "UMAP Dimension 1", y = "UMAP Dimension 2") +
  scale_color_viridis(discrete = TRUE) +
  theme_minimal() +  
  theme(plot.title = element_text(hjust = 0.5)) 
ggplotly(umap2)

# save the plot
plot_filename <- file.path(plots_folder, "Umap_concept.png")
ggsave(plot_filename, umap2, width = 8, height = 6, dpi = 300) 

5 Euclidian distance between segments

Let’s take a look on the inter-segment distance. Some of the concepts’ segments might travel through space more than others

Let’s take distance of segments based on the features Better don’t run it, takes an hour or so. Load the interseg df

# Euclidean distance between segments and feature set
# distance segement1(s1) with segment2 (s2) is the distance between a feature set if s1 (f1) and s2 (f2), where distance(s1,s2) = sqrt(f1_feature1-f2_feature1)^2+...+  (f1_featurei-f2_featurej)^2)

#x = vector of features 1
#y = vector of features 2
#distance = sqrt(sum((x-y)^2))

# delete the category for now
data_dist <- data %>%
  select(-category)

# distances
calculate_distance <- function(features_s1, features_s2) {
  # Calculate the squared differences for each feature
  squared_differences <- (features_s1 - features_s2)^2
  # Calculate the sum of squared differences
  sum_squared_differences <- sum(squared_differences)
  # Calculate the distance by taking the square root
  eucl_distance <- sqrt(sum_squared_differences)
  
  return(eucl_distance)
}

#calculate_distance(data_dist[1,7:ncol(data_dist)], data_dist[2,7:ncol(data_dist)])

calculate_distances_to_all <- function(target_segment_index, dataset, start_col) {
  target_segment <- dataset[target_segment_index, start_col:ncol(dataset)]
  distances <- numeric(nrow(dataset))
  print
  for (i in 1:nrow(dataset)) {
    distances[i] <- calculate_distance(target_segment, dataset[i, start_col:ncol(dataset)])
  }
  
  return(distances)
}


interSeg_distance <- data.frame(matrix(0, nrow = nrow(data_dist), ncol = nrow(data_dist)))

# make individual ID for each segment
data_dist <- data_dist %>%
  mutate(ID = paste(participant, trial, segmentCount, sep = "_")) %>%
  select(ID, everything())

# calculate distance between each segment
for (s in 1:nrow(data_dist)) {
  id <- data_dist$ID[s]  # collect ID for that specific row/column
  interSeg_distance_vector <- calculate_distances_to_all(s, data_dist, 8) 
  rownames(interSeg_distance)[s] <- id  # set row name
  colnames(interSeg_distance)[s] <- id  # set column name
  interSeg_distance[s,] <- interSeg_distance_vector
  
}

# save the file
output_file <- paste0(dataset_folder, "/distance_matrix.csv")
write.csv(interSeg_distance, file = output_file, row.names = FALSE)

Let’s now get for each event list of IDs, so we can index those in distance matrix to get mean distances

IDs_list <- data_dist %>%
  group_by(trial, participant) %>%
  summarise(Unique_IDs = list(unique(ID))) %>%
  ungroup()

## `summarise()` has grouped output by 'trial'. You can override using the
## `.groups` argument.

# vector to store mean distances
mean_distances <- numeric()

# loop through each combination in the df
for (i in 1:nrow(IDs_list)) {
  # list of unique IDs for the current combination
  id_list <- IDs_list$Unique_IDs[[i]]
  
  # get the relevant rows and columns in the distance matrix
  subset_matrix <- interSeg_distance[id_list, id_list]

  # mean distance for the current combination
  mean_segments <- sapply(subset_matrix, mean, na.rm = TRUE) # mean(df) does not work
  mean_distance <- mean(mean_segments)
  
  # append the mean distance to the vector
  mean_distances <- c(mean_distances, mean_distance)
}


# append the mean distance within trial to the ID list
IDs_list$Dist_mean <- NA
IDs_list$Dist_mean <- mean_distances

# merge the original df with avg distance
data_dist_avg <- merge(data_dist, IDs_list, by.x = c("trial", "participant"), by.y = c("trial", "participant"))

# what is the distribution
hist(IDs_list$Dist_mean)

6 Visualization

Prepare some more info into our df

# plot the avg distance between segments per category

# first we need to put back category
data_dist_avg <- merge(data_dist_avg, concept_category_mapping, by.x = "word", by.y = "concept", all.x = TRUE)

# delete the unique list so we can save it
data_dist_avg <- data_dist_avg %>%
  select(-Unique_IDs)

# save the df
output_file <- paste0(dataset_folder, "/meanDist_pertrial.csv")
write.csv(data_dist_avg, file = output_file, row.names = FALSE)

# load the distance df if you don't have it
# data_dist_avg_path <- file.path(dataset_folder, "/meanDist_pertrial.csv")
# data_dist_avg <- read_csv(data_dist_avg_path)

Plot of distance between segments per category

# plot of distances between segments across categories
plot1 <- ggplot(data_dist_avg, aes(x = reorder(category, -Dist_mean), y = Dist_mean)) +
  geom_point(size = 2, color = viridis(1, option = "A"), position = position_dodge(width = 0.5)) +
  geom_boxplot(width = 0.2, fill = viridis(1), alpha = 0.5, position = position_dodge(width = 0.5)) +
  labs(title = "Average Distance Between Segments Across Categories",
       x = "Category", y = "Average Distance per Trial") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none")

plot1

# save it
plot_filename <- file.path(plots_folder, "MeanDist_categ.png")
ggsave(plot_filename, plot1, width = 8, height = 6, dpi = 300)

Plot of distance between segments per concept

# plot the same but for each concept
plot2 <- ggplot(data_dist_avg, aes(x = reorder(word, -Dist_mean), y = Dist_mean)) +
  geom_point(size = 2, color = viridis(1, option = "B"), position = position_dodge(width = 0.5)) +
  geom_boxplot(width = 0.2, fill = viridis(1), alpha = 0.5, position = position_dodge(width = 0.5)) +
  labs(title = "Average Distance Between Segments Across Concepts",
       x = "Concept", y = "Average Distance per Trial") +
  theme_minimal() +
theme(
    plot.title = element_text(hjust = 0.5),
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    legend.text = element_text(size = 20)  
  ) 

plot2

# save it
plot_filename <- file.path(plots_folder, "MeanDist_concept.png")
ggsave(plot_filename, plot2, width = 8, height = 6, dpi = 300) 

7 Additional analysis

7.1 Distance within participants

Let’s now take a look not what is the variability within trial, but within a participant

IDs_list_pcn <- data_dist %>%
  group_by(participant) %>%
  summarise(Unique_IDs = list(unique(ID))) %>%
  ungroup()

# vector to store mean distances
mean_distances_pcn <- numeric()

# loop over IDs
for (i in 1:nrow(IDs_list_pcn)) {
  # list of IDs
  id_list <- IDs_list_pcn$Unique_IDs[[i]]
  
  # select relevant rows/columns from the matrix
  subset_matrix <- interSeg_distance[id_list, id_list]

  # calculate the mean distance for the current combination
  mean_segments <- sapply(subset_matrix, mean, na.rm = TRUE) 
  mean_distance <- mean(mean_segments)
  
  # Append the mean distance to the vector
  mean_distances_pcn <- c(mean_distances_pcn, mean_distance)
}


# append the mean distance within trial to the ID list
IDs_list_pcn$Dist_mean <- NA
IDs_list_pcn$Dist_mean <- mean_distances_pcn


# merge the original df with avg distance
data_dist_avg_pcn <- merge(data_dist, IDs_list_pcn, by.x = "participant", by.y = "participant")
data_dist_avg_pcn

# merge with original df
data_dist_avg_pcn <- merge(data_dist_avg_pcn, concept_category_mapping, by.x = "word", by.y = "concept", all.x = TRUE)

# delete the unique list so we can save it
data_dist_avg_pcn <- data_dist_avg_pcn %>%
  select(-Unique_IDs)

# save it
output_file <- paste0(dataset_folder, "/meanDist_perpcn.csv")
write.csv(data_dist_avg_pcn, file = output_file, row.names = FALSE)

Plot it

# distance per categories
plot3 <- ggplot(data_dist_avg_pcn, aes(x = reorder(category, -Dist_mean), y = Dist_mean)) +
  geom_point(size = 2, color = viridis(1, option = "A"), position = position_dodge(width = 0.5)) +
  geom_boxplot(width = 0.2, fill = viridis(1), alpha = 0.5, position = position_dodge(width = 0.5)) +
  labs(title = "Average Distance Between Segments Across Categories",
       x = "Category", y = "Average Distance per Participant") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none")

plot3 

# save it
plot_filename <- file.path(plots_folder, "MeanDist_categ_pcn.png")
ggsave(plot_filename, plot3, width = 8, height = 6, dpi = 300) 

# plot the same but for each concept
plot4 <- ggplot(data_dist_avg_pcn, aes(x = reorder(word, -Dist_mean), y = Dist_mean)) +
  geom_point(size = 2, color = viridis(1, option = "B"), position = position_dodge(width = 0.5)) +
  geom_boxplot(width = 0.2, fill = viridis(1), alpha = 0.5, position = position_dodge(width = 0.5)) +
  labs(title = "Average Distance Between Segments Across Concepts",
       x = "Concept", y = "Average Distance per Participant") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5), legend.position = "none", 
        axis.text.x = element_text(angle = 45, hjust = 1)) 

plot4

# save
plot_filename <- file.path(plots_folder, "MeanDist_concept_pcn.png")
ggsave(plot_filename, plot4, width = 8, height = 6, dpi = 300)